Automated User Story Generation with Test Case Specification Using Large Language Model

TL;DR

This paper presents GeneUS, a tool leveraging GPT-4.0 to automatically generate user stories from requirements documents, streamlining requirements engineering and reducing manual effort in software development workflows.

Contribution

The paper introduces a novel AI-based tool that automates user story creation from requirements documents using large language models, enhancing efficiency in requirements engineering.

Findings

GeneUS successfully generates user stories in JSON format.

Automating user story creation reduces manual effort and stakeholder meetings.

The approach integrates seamlessly with existing project management tools.

Abstract

Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a proposed scope of work documented in different forms. RE phase ends with a list of user-stories for each unit task identified through discussions and usually these are created and tracked on a project management tool such as Jira, AzurDev etc. In this research we developed a tool "GeneUS" using GPT-4.0 to automatically create user stories from requirements document which is the outcome of the RE phase. The output is provided in JSON format leaving the possibilities open for downstream integration to the popular project management tools. Analyzing requirements documents takes significant effort and multiple meetings with stakeholders. We believe, automating this process will certainly reduce additional load off the software engineers, and increase the productivity since they will be able to utilize their time on other prioritized tasks.